The lab has positions available for PhD students and postdoctoral fellows in the following areas:

1. Quantitative cell biology. The lab applies quantitative single-cell imaging to study dynamic processes in endocytosis, membrane composition, cell signalling, and, more recently, in the regulation of liquid phase transitions during nonmembrane-bound compartmentalisation. We also investigate the largely unappreciated roles that cellular structures and compartmentalisation may have in signal processing by acting as capacitors and passive noise filters.

2. Single-cell approaches. We are continuously striving to increase the amount of molecular and phenotypic measurements that we can extract from single cells within their natural microenvironment, be it in cell populations grown in vitro or in tissues. A major goal is to achieve image-based multiplexing of large numbers of transcript and protein measurements in thousands of single cells at very high spatial resolution.

3. Cell-to-cell variability and emerging cell population phenomena. Since our discovery that cell-to-cell variability in genetically identical human cell populations does not emerge randomly but follows largely predictive rules, we investigate the mechanisms by which this occurs, and how patterns can emerge. We harness the natural variability in single-cell measurements to identify sources of variability in cellular activities, and to uncover the molecular circuits by which these activities are adapted to cellular state and microenvironment.

4. Subcellular organisation of the transcriptome. Having developed a platform for high-throughput RNA FISH against thousands of genes in thousands of single cells at single-molecule resolution, we have the unique opportunity to study the largely unknown mesoscale subcellular organisation of the transcriptome, which possibly involves mechanisms of non membrane-bound compartmentalisation (see above), to uncover which factors and transcript properties are involved in this process, and how this impacts gene expression.

4. Large-scale genetic perturbations. An important tool in the lab is the use of high-throughput gene perturbation to study the effects of large sets of perturbations in large numbers of single cells with quantitative multivariate readouts. Such datasets allow the generation of systems-level views on cellular processes. Besides RNA interference and small compounds, we are looking into new ways of high-throughput gene deletion in human cells with single-cell resolution.

5. Network biology. Large datasets collected by ourselves often form the basis to study biological phenomena from a network perspective, in which the networks can consist of perturbations, of cells, or of phenotypic or molecular measurements. We continuously develop new statistical and computational methods to infer interactions between genes, perturbations, or phenotypes, inspired by our experience in molecular cell biology.


Please contact Lucas Pelkmans directly if you are interested.


Postdocs are asked to send minimal two letters of reference (one preferably from the PhD supervisor) and a motivation letter.

PhD students will be asked to apply via the PhD programs in Systems Biology or Molecular Life Sciences. Please check their websites for admission requirements, the selection process, and interviewing possibilities.

The lab operates under the philosophy that people do both experiments and computational work, and places just as much importance on the quality and originality of experiments and the collected data as on the statistical methods and theoretical models. As a consequence, the lab provides a highly interdisciplinary research environment with members from multiple backgrounds.


Some recent publications from the lab on these topics:

  • Stoeger T, Battich N, Pelkmans L. Passive Noise Filtering by Cellular Compartmentalization. Cell (2016)
  • Battich N, Stoeger T, Pelkmans L. Control of Transcript Variability in Single Mammalian Cells. Cell (2015)
  • Frechin M, Stoeger T, Daetwyler S, Gehin C, Battich N, Damm EM, Stergiou L, Riezman H, Pelkmans L. Cell-intrinsic adaptation of lipid composition to local crowding drives social behaviour. Nature (2015)
  • Liberali P, Snijder B, Pelkmans L. A hierarchical map of regulatory genetic interactions in membrane trafficking. Cell (2014)
  • Battich N, Stoeger T, Pelkmans L. Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nat Methods (2013)
  • Snijder B, Liberali P, Frechin M, Stoeger T, Pelkmans L. Predicting functional gene interactions with the hierarchical interaction score. Nat Methods (2013)
  • Wippich F, Bodenmiller B, Trajkovska MG, Wanka S, Aebersold R, Pelkmans L. Dual specificity kinase DYRK3 couples stress granule condensation/dissolution to mTORC1 signaling. Cell (2013)